TY  - EJOU
AU  - Wang, Jiali 
AU  - Li, Bing 
AU  - Qiu, Chengyu 
AU  - Zhang, Xinyun 
AU  - Cheng, Yuting 
AU  - Wang, Peihua 
AU  - Zhou, Ta 
AU  - Ge, Hong 
AU  - Zhang, Yuanpeng 
AU  - Cai, Jing 

TI  - Multi-View \& Transfer Learning for Epilepsy Recognition Based on EEG Signals
T2  - Computers, Materials \& Continua

PY  - 2023
VL  - 75
IS  - 3
SN  - 1546-2226

AB  - Epilepsy is a central nervous system disorder in which brain activity becomes abnormal. Electroencephalogram (EEG) signals, as recordings of brain activity, have been widely used for epilepsy recognition. To study epileptic EEG signals and develop artificial intelligence (AI)-assist recognition, a multi-view transfer learning (MVTL-LSR) algorithm based on least squares regression is proposed in this study. Compared with most existing multi-view transfer learning algorithms, MVTL-LSR has two merits: (1) Since traditional transfer learning algorithms leverage knowledge from different sources, which poses a significant risk to data privacy. Therefore, we develop a knowledge transfer mechanism that can protect the security of source domain data while guaranteeing performance. (2) When utilizing multi-view data, we embed view weighting and manifold regularization into the transfer framework to measure the views’ strengths and weaknesses and improve generalization ability. In the experimental studies, 12 different simulated multi-view & transfer scenarios are constructed from epileptic EEG signals licensed and provided by the University of Bonn, Germany. Extensive experimental results show that MVTL-LSR outperforms baselines. The source code will be available on .
KW  - Multi-view learning; transfer learning; least squares regression; epilepsy; EEG signals

DO  - 10.32604/cmc.2023.037457